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    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# !pip install plotly\n",
    "try:\n",
    "    # https://stackoverflow.com/questions/57105747/modulenotfounderror-no-module-named-plotly-graph-objects/57112843\n",
    "    #     import plotly.graph_objects as go\n",
    "    #     import plotly.express as px\n",
    "    import plotly.express as px\n",
    "    import plotly.graph_objects as go\n",
    "except ImportError as e:\n",
    "    from plotly import graph_objs as go\n",
    "    from plotly import express as px\n",
    "# import plotly.express as px\n",
    "# import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from datetime import timedelta\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.api import Holt, SimpleExpSmoothing, ExponentialSmoothing\n",
    "# from fbprophet import Prophet\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "# !pip install pyramid-arima\n",
    "# from pyramid.arima import auto_arima\n",
    "std = StandardScaler()\n",
    "# pd.set_option('display.float_format', lambda x: '%.6f' % x)\n",
    "# out\n",
    "# filename=r\"G:\\file\\学校\\可视化\\大作业\\COVID-19\\COVID-19-Data-master\\US\\County_level_summary\\US_County_summary_covid19_confirmed_transpose.csv\"\n",
    "\n",
    "state_filename_base = r\"G:\\file\\学校\\可视化\\大作业\\COVID-19\\COVID-19-Data-master\\US\\State_level_summary\\US_State_summary_covid19_{}_trpo.xlsx\"\n",
    "# state_filename_base=r\"COVID-19-Data-master\\US\\State_level_summary\\US_State_summary_covid19_{}_trpo.xlsx\"\n",
    "# state_filename_base=r\"COVID-19-Data-master/US/State_level_summary/US_State_summary_covid19_{}_trpo.xlsx\"\n",
    "filename = r\"G:\\file\\学校\\可视化\\大作业\\COVID-19\\GIS疫情地图2020全年-至今数据\\【GIS点滴疫情地图·2020年01月02日-2021年01月25日】国内每天疫情统计.xlsx\"\n",
    "\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "\n",
    "def get_sum(type_name):\n",
    "    df = pd.read_excel(state_filename_base.format(type_name))\n",
    "    # pd 每一列 求和\n",
    "    df_sum = df.sum()\n",
    "    # print(\"df_sum\")\n",
    "    # print(df_sum)\n",
    "    return df_sum\n",
    "\n",
    "\n",
    "def get_3type_df():\n",
    "    df_confirmed = pd.read_excel(state_filename_base.format(\"confirmed\"))\n",
    "    df_recovered = pd.read_excel(state_filename_base.format(\"recovered\"))\n",
    "    df_death = pd.read_excel(state_filename_base.format(\"death\"))\n",
    "    # df=\n",
    "\n",
    "    df = pd.DataFrame({})\n",
    "    df[\"confirmed\"] = get_sum(\"confirmed\")\n",
    "    df[\"recovered\"] = get_sum(\"recovered\")\n",
    "    df[\"death\"] = get_sum(\"death\")\n",
    "    # print(\"df_death\")\n",
    "    # print(df_death)\n",
    "    # print(\"df_death.shape\")\n",
    "    # print(df_death.shape)\n",
    "    return df\n",
    "\n",
    "\n",
    "# covid = get_3type_df()\n",
    "covid=pd.read_excel(filename)\n",
    "# confirmed_col = \"confirmed\"\n",
    "# recovered_col = \"recovered\"\n",
    "# death_col = \"death\"\n",
    "\n",
    "confirmed_col = \"diagnose\"\n",
    "recovered_col = \"cure\"\n",
    "death_col = \"death\"\n",
    "\n",
    "X = covid[[confirmed_col,recovered_col,death_col]]\n",
    "# 死亡率 Mortality\n",
    "# Standard Scaling since K-Means Clustering is a distance based alogrithm\n",
    "# 标准缩放，因为K-均值聚类是一种基于距离的算法\n",
    "X = std.fit_transform(X)\n",
    "\n",
    "import  util\n",
    "# util.elbowMethod(X)\n",
    "# util.hierarchicalClusteringTest(X)\n",
    "cluster_summary=util.final_KMeans(X,2,covid)\n",
    "# print(cluster_summary)\n",
    "\n",
    "\n",
    "def get_x():\n",
    "    datewise = covid\n",
    "    # yy=datewise[confirmed_col]-datewise[recovered_col]-datewise[death_col]\n",
    "    # print(\"yy\")\n",
    "    # print(yy)\n",
    "    # print(\"datewise.index\")\n",
    "    # print(datewise.index)\n",
    "    # 总的 和\n",
    "    # 根据不同的 couty\n",
    "    # 确诊的 - 治愈的 - 死亡的\n",
    "    # 就是现在还在患病的\n",
    "    # 分配  Distribution 分布\n",
    "    countrywise = datewise\n",
    "    # countrywise[\"Mortality\"]=(countrywise[\"Deaths\"]/countrywise[\"Confirmed\"])*100\n",
    "    # countrywise[\"Recovery\"]=(countrywise[\"Recovered\"]/countrywise[\"Confirmed\"])*100\n",
    "\n",
    "    countrywise[\"Mortality\"] = (countrywise[death_col] / countrywise[confirmed_col]) * 100\n",
    "    countrywise[\"Recovery\"] = (countrywise[recovered_col] / countrywise[confirmed_col]) * 100\n",
    "\n",
    "    # fig=px.bar(x=datewise.index,y=datewise[confirmed_col]-datewise[recovered_col]-datewise[death_col])\n",
    "    # fig.update_layout(title=\"Distribution of Number of Active Cases 累计患病的分布(各个县)\",\n",
    "    #                   xaxis_title=\"县\",yaxis_title=\"Number of Cases 患病的个数\",)\n",
    "    # # xaxis_title=\"Date\",yaxis_title=\"Number of Cases\",\n",
    "    # fig.show()\n",
    "\n",
    "    # 正在患病的分布(各个县)\"\n",
    "    # 为什么没有显示呢\n",
    "\n",
    "    X = countrywise[[\"Mortality\", \"Recovery\"]]\n",
    "    # 死亡率 Mortality\n",
    "    # Standard Scaling since K-Means Clustering is a distance based alogrithm\n",
    "    # 标准缩放，因为K-均值聚类是一种基于距离的算法\n",
    "    X = std.fit_transform(X)\n",
    "\n",
    "# wcss = []\n",
    "# sil = []\n",
    "# for i in range(2, 11):\n",
    "#     # 分类的个数 去尝试 每种尝试 发现 2 3 会好一些\n",
    "#     # 再根据层次聚类图 我们认为 选择分成3类比较好\n",
    "#     clf = KMeans(n_clusters=i, init='k-means++', random_state=42)\n",
    "#     clf.fit(X)\n",
    "#     labels = clf.labels_\n",
    "#     centroids = clf.cluster_centers_\n",
    "#     sil.append(silhouette_score(X, labels, metric='euclidean'))\n",
    "#     wcss.append(clf.inertia_)\n",
    "\n",
    "# 肘部法则\n",
    "# def ElbowMethod():\n",
    "#     x = np.arange(2, 11)\n",
    "#     plt.figure(figsize=(10, 5))\n",
    "#     plt.plot(x, wcss, marker='o')\n",
    "#     plt.xlabel(\"Number of Clusters 集群的个数 \")\n",
    "#     # 集群;群集;\n",
    "#     plt.ylabel(\"Within Cluster Sum of Squares (WCSS) 簇内平方和\")\n",
    "#     # 簇内平方和（WCSS）\n",
    "#     plt.title(\"Elbow Method 肘部法则\")\n",
    "#     # –Elbow Method和轮廓...\n",
    "#     # 肘部法则\n",
    "#     plt.show()\n",
    "\n",
    "\n",
    "# countrywise[\"Mortality\"]=(countrywise[\"Deaths\"]/countrywise[\"Confirmed\"])*100\n",
    "# countrywise[\"Recovery\"]=(countrywise[\"Recovered\"]/countrywise[\"Confirmed\"])*100\n",
    "\n",
    "import scipy.cluster.hierarchy as sch\n",
    "\n",
    "\n",
    "# 等级制度(尤指社会或组织); 统治集团; 层次体系; hierarchy\n",
    "#\n",
    "# 层次聚类测试\n",
    "def HierarchicalClusteringTest():\n",
    "    plt.figure(figsize=(20, 15))\n",
    "    # dendrogram 系统树图（一种表示亲缘关系的树状图解）;\n",
    "    # 连接; 联系; 链环; 连锁; 联动装置; linkage\n",
    "\n",
    "    dendogram = sch.dendrogram(sch.linkage(X, method=\"ward\"))\n",
    "    # dendogram.\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# clf_final = KMeans(n_clusters=3, init='k-means++', random_state=6)\n",
    "# clf_final.fit(X)\n",
    "#\n",
    "# # 分类\n",
    "# countrywise[\"Clusters\"] = clf_final.predict(X)\n",
    "#\n",
    "# cluster_summary = pd.concat([countrywise[countrywise[\"Clusters\"] == 1].head(15),\n",
    "#                              countrywise[countrywise[\"Clusters\"] == 2].head(15),\n",
    "#                              countrywise[countrywise[\"Clusters\"] == 0].head(15)])\n",
    "# cluster_summary.style.background_gradient(cmap='Reds').format(\"{:.2f}\")\n",
    "# # 背景梯度\n",
    "# # plt.show()\n",
    "# print(\"cluster_summary\")\n",
    "# print(cluster_summary)\n",
    "# 数据显示  治愈率是0 这是\n",
    "# 我们把这些州 按照治愈率和 死亡率 分成了三类\n",
    "# 根据背景梯度图 显示 一类是死亡率较高 0-死亡率其次 , 2-死亡率最低\n",
    "\n",
    "\n",
    "\n"
   ]
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       "      <td>1</td>\n",
       "      <td>1578880800</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>中国</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1578967200</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>中国</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1579053600</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>中国</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1579140000</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>中国</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1579226400</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>中国</td>\n",
       "      <td>198</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1579312800</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>中国</td>\n",
       "      <td>201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1579399200</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>中国</td>\n",
       "      <td>218</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1579485600</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>中国</td>\n",
       "      <td>291</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1579572000</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1268</td>\n",
       "      <td>中国</td>\n",
       "      <td>544</td>\n",
       "      <td>137</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>1579708500</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>中国</td>\n",
       "      <td>634</td>\n",
       "      <td>422</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>1579786500</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16</td>\n",
       "      <td>中国</td>\n",
       "      <td>897</td>\n",
       "      <td>1076</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>1579881300</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>17</td>\n",
       "      <td>中国</td>\n",
       "      <td>1408</td>\n",
       "      <td>2032</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>1579967705</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40935</td>\n",
       "      <td>中国</td>\n",
       "      <td>2076</td>\n",
       "      <td>2692</td>\n",
       "      <td>49</td>\n",
       "      <td>56</td>\n",
       "      <td>1580054100</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id name  diagnose  suspect   cure  death        time  label  \\\n",
       "42  300837   中国     77048     4148  23183   2445  1582473300      1   \n",
       "43  310917   中国     77269     3434  25007   2596  1582559700      1   \n",
       "44  320997   中国     77785     2824  27655   2666  1582646100      1   \n",
       "45  331042   中国     78195     2491  30078   2718  1582732500      1   \n",
       "46  341122   中国     78631     2358  32916   2747  1582818908      1   \n",
       "47  351202   中国     78962     2308  36312   2791  1582905300      1   \n",
       "48  361282   中国     79394     1418  39308   2838  1582991700      1   \n",
       "49  371327   中国     79972      851  42162   2873  1583078100      1   \n",
       "50  381407   中国     80175      715  44845   2915  1583164500      1   \n",
       "51  391487   中国     80303      587  47434   2948  1583250902      1   \n",
       "52  401532   中国     80424      520  50010   2984  1583337300      1   \n",
       "53  411507   中国     80581      522  52305   3016  1583423700      1   \n",
       "54  421587   中国     80734      482  53968   3045  1583510100      1   \n",
       "55  431632   中国     80815      502  55558   3073  1583596500      1   \n",
       "56  441712   中国     80868      458  57412   3101  1583682900      1   \n",
       "0        1   中国        41        0      0      1  1578794400      1   \n",
       "1        2   中国        41        0      0      1  1578880800      1   \n",
       "2        3   中国        41        0      0      1  1578967200      1   \n",
       "3        4   中国        41        0      0      2  1579053600      1   \n",
       "4        5   中国        45        0      0      2  1579140000      1   \n",
       "5        6   中国        62        0      0      2  1579226400      1   \n",
       "6        7   中国       198        0      0      3  1579312800      1   \n",
       "7        8   中国       201        0      0      3  1579399200      1   \n",
       "8        9   中国       218        6      0      4  1579485600      1   \n",
       "9       11   中国       291        9      0      4  1579572000      1   \n",
       "10    1268   中国       544      137     28     17  1579708500      1   \n",
       "11      15   中国       634      422     30     17  1579786500      1   \n",
       "12      16   中国       897     1076     36     26  1579881300      1   \n",
       "13      17   中国      1408     2032     39     42  1579967705      1   \n",
       "14   40935   中国      2076     2692     49     56  1580054100      1   \n",
       "\n",
       "    seriousCount  currentdiagnose         day  Clusters  \n",
       "42       10968.0          51420.0  2020-02-23         0  \n",
       "43        9915.0          49666.0  2020-02-24         0  \n",
       "44        9126.0          47464.0  2020-02-25         0  \n",
       "45        8752.0          45399.0  2020-02-26         0  \n",
       "46        8346.0          42968.0  2020-02-27         0  \n",
       "47        7952.0          39859.0  2020-02-28         0  \n",
       "48        7664.0          37248.0  2020-02-29         0  \n",
       "49        7365.0          34937.0  2020-03-01         0  \n",
       "50        7110.0          32415.0  2020-03-02         0  \n",
       "51        6806.0          29921.0  2020-03-03         0  \n",
       "52        6416.0          27430.0  2020-03-04         0  \n",
       "53        5952.0          25260.0  2020-03-05         0  \n",
       "54        5737.0          23721.0  2020-03-06         0  \n",
       "55        5489.0          22184.0  2020-03-07         0  \n",
       "56        5264.0          20355.0  2020-03-08         0  \n",
       "0            NaN              NaN  2020-01-12         1  \n",
       "1            NaN              NaN  2020-01-13         1  \n",
       "2            NaN              NaN  2020-01-14         1  \n",
       "3            NaN              NaN  2020-01-15         1  \n",
       "4            NaN              NaN  2020-01-16         1  \n",
       "5            NaN              NaN  2020-01-17         1  \n",
       "6            NaN              NaN  2020-01-18         1  \n",
       "7            NaN              NaN  2020-01-19         1  \n",
       "8            NaN              NaN  2020-01-20         1  \n",
       "9            NaN              NaN  2020-01-21         1  \n",
       "10           NaN              NaN  2020-01-22         1  \n",
       "11           NaN              NaN  2020-01-23         1  \n",
       "12           NaN              NaN  2020-01-24         1  \n",
       "13           NaN              NaN  2020-01-25         1  \n",
       "14           NaN              NaN  2020-01-26         1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Unknown format code 'f' for object of type 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mD:\\software\\anaconda\\envs\\visual\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    346\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    347\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\software\\anaconda\\envs\\visual\\lib\\site-packages\\pandas\\io\\formats\\style.py\u001b[0m in \u001b[0;36m_repr_html_\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    214\u001b[0m         \u001b[0mHooks\u001b[0m \u001b[0minto\u001b[0m \u001b[0mJupyter\u001b[0m \u001b[0mnotebook\u001b[0m \u001b[0mrich\u001b[0m \u001b[0mdisplay\u001b[0m \u001b[0msystem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m         \"\"\"\n\u001b[1;32m--> 216\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m     def render(\n",
      "\u001b[1;32mD:\\software\\anaconda\\envs\\visual\\lib\\site-packages\\pandas\\io\\formats\\style.py\u001b[0m in \u001b[0;36mrender\u001b[1;34m(self, sparse_index, sparse_columns, **kwargs)\u001b[0m\n\u001b[0;32m    270\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msparse_columns\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    271\u001b[0m             \u001b[0msparse_columns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_option\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"styler.sparse.columns\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 272\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_render_html\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msparse_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msparse_columns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    273\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    274\u001b[0m     def set_tooltips(\n",
      "\u001b[1;32mD:\\software\\anaconda\\envs\\visual\\lib\\site-packages\\pandas\\io\\formats\\style_render.py\u001b[0m in \u001b[0;36m_render_html\u001b[1;34m(self, sparse_index, sparse_columns, **kwargs)\u001b[0m\n\u001b[0;32m    121\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    122\u001b[0m         \u001b[1;31m# TODO: namespace all the pandas keys\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 123\u001b[1;33m         \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_translate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msparse_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msparse_columns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    124\u001b[0m         \u001b[0md\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    125\u001b[0m         return self.template_html.render(\n",
      "\u001b[1;32mD:\\software\\anaconda\\envs\\visual\\lib\\site-packages\\pandas\\io\\formats\\style_render.py\u001b[0m in \u001b[0;36m_translate\u001b[1;34m(self, sparse_index, sparse_cols, blank)\u001b[0m\n\u001b[0;32m    226\u001b[0m             \u001b[0mmax_cols\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m             \u001b[0mTRIMMED_ROW_CLASS\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 228\u001b[1;33m             \u001b[0mTRIMMED_COL_CLASS\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    229\u001b[0m         )\n\u001b[0;32m    230\u001b[0m         \u001b[0md\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"body\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\software\\anaconda\\envs\\visual\\lib\\site-packages\\pandas\\io\\formats\\style_render.py\u001b[0m in \u001b[0;36m_translate_body\u001b[1;34m(self, data_class, row_heading_class, sparsify_index, max_rows, max_cols, trimmed_row_class, trimmed_col_class)\u001b[0m\n\u001b[0;32m    512\u001b[0m                     \u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhidden_columns\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhidden_rows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    513\u001b[0m                     \u001b[0mattributes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 514\u001b[1;33m                     \u001b[0mdisplay_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_display_funcs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    515\u001b[0m                 )\n\u001b[0;32m    516\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\software\\anaconda\\envs\\visual\\lib\\site-packages\\pandas\\io\\formats\\style_render.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m    975\u001b[0m     \u001b[1;31m# Get initial func from input string, input callable, or from default factory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    976\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 977\u001b[1;33m         \u001b[0mfunc_0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    978\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    979\u001b[0m         \u001b[0mfunc_0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Unknown format code 'f' for object of type 'str'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2ca3046bdc8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_summary.style.background_gradient(cmap='Reds').format(\"{:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "  color: #f1f1f1;\n",
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       "#T_00705_row0_col2, #T_00705_row9_col5 {\n",
       "  background-color: #7e0610;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row0_col3, #T_00705_row0_col8, #T_00705_row0_col9, #T_00705_row11_col2, #T_00705_row12_col2, #T_00705_row13_col2, #T_00705_row14_col0, #T_00705_row14_col2, #T_00705_row14_col4, #T_00705_row14_col5, #T_00705_row14_col6, #T_00705_row15_col11, #T_00705_row16_col11, #T_00705_row17_col11, #T_00705_row18_col11, #T_00705_row19_col11, #T_00705_row20_col11, #T_00705_row21_col11, #T_00705_row22_col11, #T_00705_row23_col11, #T_00705_row24_col11, #T_00705_row25_col11, #T_00705_row26_col11, #T_00705_row27_col11, #T_00705_row28_col11, #T_00705_row29_col11 {\n",
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       "#T_00705_row0_col7, #T_00705_row0_col11, #T_00705_row1_col7, #T_00705_row1_col11, #T_00705_row2_col7, #T_00705_row2_col11, #T_00705_row3_col7, #T_00705_row3_col11, #T_00705_row4_col7, #T_00705_row4_col11, #T_00705_row5_col7, #T_00705_row5_col11, #T_00705_row6_col7, #T_00705_row6_col11, #T_00705_row7_col7, #T_00705_row7_col11, #T_00705_row8_col7, #T_00705_row8_col11, #T_00705_row9_col7, #T_00705_row9_col11, #T_00705_row10_col7, #T_00705_row10_col11, #T_00705_row11_col7, #T_00705_row11_col11, #T_00705_row12_col7, #T_00705_row12_col11, #T_00705_row13_col7, #T_00705_row13_col11, #T_00705_row14_col7, #T_00705_row14_col8, #T_00705_row14_col9, #T_00705_row14_col11, #T_00705_row15_col0, #T_00705_row15_col2, #T_00705_row15_col3, #T_00705_row15_col4, #T_00705_row15_col5, #T_00705_row15_col6, #T_00705_row15_col7, #T_00705_row16_col0, #T_00705_row16_col2, #T_00705_row16_col3, #T_00705_row16_col4, #T_00705_row16_col5, #T_00705_row16_col7, #T_00705_row17_col0, #T_00705_row17_col2, #T_00705_row17_col3, #T_00705_row17_col4, #T_00705_row17_col5, #T_00705_row17_col7, #T_00705_row18_col0, #T_00705_row18_col2, #T_00705_row18_col3, #T_00705_row18_col4, #T_00705_row18_col5, #T_00705_row18_col7, #T_00705_row19_col0, #T_00705_row19_col2, #T_00705_row19_col3, #T_00705_row19_col4, #T_00705_row19_col5, #T_00705_row19_col7, #T_00705_row20_col0, #T_00705_row20_col2, #T_00705_row20_col3, #T_00705_row20_col4, #T_00705_row20_col5, #T_00705_row20_col7, #T_00705_row21_col0, #T_00705_row21_col2, #T_00705_row21_col3, #T_00705_row21_col4, #T_00705_row21_col5, #T_00705_row21_col7, #T_00705_row22_col0, #T_00705_row22_col2, #T_00705_row22_col3, #T_00705_row22_col4, #T_00705_row22_col5, #T_00705_row22_col7, #T_00705_row23_col0, #T_00705_row23_col2, #T_00705_row23_col3, #T_00705_row23_col4, #T_00705_row23_col5, #T_00705_row23_col7, #T_00705_row24_col0, #T_00705_row24_col2, #T_00705_row24_col3, #T_00705_row24_col4, #T_00705_row24_col5, #T_00705_row24_col7, #T_00705_row25_col0, #T_00705_row25_col4, #T_00705_row25_col7, #T_00705_row26_col0, #T_00705_row26_col4, #T_00705_row26_col7, #T_00705_row27_col0, #T_00705_row27_col4, #T_00705_row27_col7, #T_00705_row28_col0, #T_00705_row28_col4, #T_00705_row28_col7, #T_00705_row29_col4, #T_00705_row29_col7 {\n",
       "  background-color: #fff5f0;\n",
       "  color: #000000;\n",
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       "#T_00705_row1_col0 {\n",
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       "  background-color: #7c0510;\n",
       "  color: #f1f1f1;\n",
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       "  background-color: #b31218;\n",
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       "#T_00705_row1_col5 {\n",
       "  background-color: #b01217;\n",
       "  color: #f1f1f1;\n",
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       "  background-color: #c4161c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row1_col8 {\n",
       "  background-color: #b71319;\n",
       "  color: #f1f1f1;\n",
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       "#T_00705_row1_col9 {\n",
       "  background-color: #820711;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row2_col0, #T_00705_row4_col9 {\n",
       "  background-color: #d11e1f;\n",
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       "  background-color: #79040f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row2_col4 {\n",
       "  background-color: #fb7050;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row2_col5 {\n",
       "  background-color: #a91016;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row2_col8 {\n",
       "  background-color: #e02c26;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row2_col9 {\n",
       "  background-color: #a50f15;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row3_col0 {\n",
       "  background-color: #cb181d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row3_col2, #T_00705_row13_col4 {\n",
       "  background-color: #77040f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row3_col3 {\n",
       "  background-color: #f14432;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row3_col4 {\n",
       "  background-color: #f96044;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row3_col5, #T_00705_row7_col6 {\n",
       "  background-color: #a30f15;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row3_col6, #T_00705_row3_col9 {\n",
       "  background-color: #b91419;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row3_col8 {\n",
       "  background-color: #f0402f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row4_col2, #T_00705_row11_col5 {\n",
       "  background-color: #75030f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row4_col3 {\n",
       "  background-color: #f4503a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row4_col4 {\n",
       "  background-color: #f44f39;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row4_col5, #T_00705_row9_col0 {\n",
       "  background-color: #9f0e14;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row4_col6 {\n",
       "  background-color: #b51318;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row4_col8 {\n",
       "  background-color: #f75b40;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row5_col0 {\n",
       "  background-color: #bd151a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row5_col2 {\n",
       "  background-color: #73030f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row5_col3 {\n",
       "  background-color: #f6553c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row5_col4 {\n",
       "  background-color: #ed392b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row5_col5 {\n",
       "  background-color: #980c13;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row5_col6, #T_00705_row7_col0 {\n",
       "  background-color: #af1117;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row5_col8, #T_00705_row7_col9 {\n",
       "  background-color: #fb7353;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row5_col9 {\n",
       "  background-color: #ee3a2c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row6_col0 {\n",
       "  background-color: #b61319;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row6_col2, #T_00705_row12_col5, #T_00705_row13_col6 {\n",
       "  background-color: #6f020e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row6_col3 {\n",
       "  background-color: #fc9d7f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row6_col4 {\n",
       "  background-color: #dd2a25;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row6_col5 {\n",
       "  background-color: #900a12;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row6_col6 {\n",
       "  background-color: #aa1016;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row6_col8 {\n",
       "  background-color: #fc8464;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row6_col9 {\n",
       "  background-color: #f7593f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row7_col2, #T_00705_row8_col2, #T_00705_row13_col5 {\n",
       "  background-color: #6b010e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row7_col3 {\n",
       "  background-color: #fdc9b3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row7_col4 {\n",
       "  background-color: #cf1c1f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row7_col5, #T_00705_row10_col6 {\n",
       "  background-color: #8a0812;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row7_col8 {\n",
       "  background-color: #fc9474;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row8_col0 {\n",
       "  background-color: #a81016;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row8_col3 {\n",
       "  background-color: #fdd2bf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row8_col4 {\n",
       "  background-color: #c2161b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row8_col5, #T_00705_row12_col4 {\n",
       "  background-color: #840711;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row8_col6 {\n",
       "  background-color: #9c0d14;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row8_col8 {\n",
       "  background-color: #fca486;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row8_col9 {\n",
       "  background-color: #fc8e6e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row9_col2, #T_00705_row10_col2 {\n",
       "  background-color: #69000d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row9_col3, #T_00705_row23_col6 {\n",
       "  background-color: #fedbcc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row9_col6, #T_00705_row11_col4 {\n",
       "  background-color: #920a13;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row9_col8 {\n",
       "  background-color: #fcb499;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row9_col9 {\n",
       "  background-color: #fca98c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row10_col0 {\n",
       "  background-color: #940b13;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row10_col3, #T_00705_row11_col3 {\n",
       "  background-color: #fee0d2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row10_col4 {\n",
       "  background-color: #a60f15;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row10_col8, #T_00705_row26_col6 {\n",
       "  background-color: #fdcab5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row10_col9 {\n",
       "  background-color: #fcc2aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row11_col0 {\n",
       "  background-color: #880811;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row11_col6 {\n",
       "  background-color: #800610;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row11_col8, #T_00705_row13_col3 {\n",
       "  background-color: #fee1d4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row11_col9, #T_00705_row24_col6 {\n",
       "  background-color: #fdd7c6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row12_col3 {\n",
       "  background-color: #fee2d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row12_col8 {\n",
       "  background-color: #fee7dc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row12_col9, #T_00705_row21_col6 {\n",
       "  background-color: #fee3d7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row13_col0 {\n",
       "  background-color: #71020e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row13_col8 {\n",
       "  background-color: #ffeee7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row13_col9 {\n",
       "  background-color: #ffebe2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row14_col3 {\n",
       "  background-color: #fee3d6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row15_col8, #T_00705_row15_col9, #T_00705_row16_col8, #T_00705_row16_col9, #T_00705_row17_col8, #T_00705_row17_col9, #T_00705_row18_col8, #T_00705_row18_col9, #T_00705_row19_col8, #T_00705_row19_col9, #T_00705_row20_col8, #T_00705_row20_col9, #T_00705_row21_col8, #T_00705_row21_col9, #T_00705_row22_col8, #T_00705_row22_col9, #T_00705_row23_col8, #T_00705_row23_col9, #T_00705_row24_col8, #T_00705_row24_col9, #T_00705_row25_col8, #T_00705_row25_col9, #T_00705_row26_col8, #T_00705_row26_col9, #T_00705_row27_col8, #T_00705_row27_col9, #T_00705_row28_col8, #T_00705_row28_col9, #T_00705_row29_col8, #T_00705_row29_col9 {\n",
       "  background-color: #000000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row16_col6, #T_00705_row28_col2, #T_00705_row29_col5 {\n",
       "  background-color: #fff2ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row17_col6 {\n",
       "  background-color: #ffefe8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row18_col6 {\n",
       "  background-color: #ffece4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row19_col6 {\n",
       "  background-color: #fee9df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row20_col6 {\n",
       "  background-color: #fee7db;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row22_col6 {\n",
       "  background-color: #fee1d3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row25_col2, #T_00705_row25_col5, #T_00705_row26_col2, #T_00705_row26_col5 {\n",
       "  background-color: #fff4ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row25_col3 {\n",
       "  background-color: #fff0e8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row25_col6 {\n",
       "  background-color: #fdcebb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row26_col3 {\n",
       "  background-color: #fee4d8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row27_col2, #T_00705_row27_col5 {\n",
       "  background-color: #fff4ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row27_col3 {\n",
       "  background-color: #fcb89e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row27_col6 {\n",
       "  background-color: #fcc4ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row28_col3 {\n",
       "  background-color: #fb6d4d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row28_col5 {\n",
       "  background-color: #fff3ed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row28_col6 {\n",
       "  background-color: #fcbea5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row29_col0 {\n",
       "  background-color: #fee6da;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row29_col2 {\n",
       "  background-color: #fff1ea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00705_row29_col3 {\n",
       "  background-color: #e83429;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00705_row29_col6 {\n",
       "  background-color: #fcb99f;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_00705_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >id</th>\n",
       "      <th class=\"col_heading level0 col1\" >name</th>\n",
       "      <th class=\"col_heading level0 col2\" >diagnose</th>\n",
       "      <th class=\"col_heading level0 col3\" >suspect</th>\n",
       "      <th class=\"col_heading level0 col4\" >cure</th>\n",
       "      <th class=\"col_heading level0 col5\" >death</th>\n",
       "      <th class=\"col_heading level0 col6\" >time</th>\n",
       "      <th class=\"col_heading level0 col7\" >label</th>\n",
       "      <th class=\"col_heading level0 col8\" >seriousCount</th>\n",
       "      <th class=\"col_heading level0 col9\" >currentdiagnose</th>\n",
       "      <th class=\"col_heading level0 col10\" >day</th>\n",
       "      <th class=\"col_heading level0 col11\" >Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row0\" class=\"row_heading level0 row0\" >42</th>\n",
       "      <td id=\"T_00705_row0_col0\" class=\"data row0 col0\" >300837</td>\n",
       "      <td id=\"T_00705_row0_col1\" class=\"data row0 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row0_col2\" class=\"data row0 col2\" >77048</td>\n",
       "      <td id=\"T_00705_row0_col3\" class=\"data row0 col3\" >4148</td>\n",
       "      <td id=\"T_00705_row0_col4\" class=\"data row0 col4\" >23183</td>\n",
       "      <td id=\"T_00705_row0_col5\" class=\"data row0 col5\" >2445</td>\n",
       "      <td id=\"T_00705_row0_col6\" class=\"data row0 col6\" >1582473300</td>\n",
       "      <td id=\"T_00705_row0_col7\" class=\"data row0 col7\" >1</td>\n",
       "      <td id=\"T_00705_row0_col8\" class=\"data row0 col8\" >10968.000000</td>\n",
       "      <td id=\"T_00705_row0_col9\" class=\"data row0 col9\" >51420.000000</td>\n",
       "      <td id=\"T_00705_row0_col10\" class=\"data row0 col10\" >2020-02-23</td>\n",
       "      <td id=\"T_00705_row0_col11\" class=\"data row0 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row1\" class=\"row_heading level0 row1\" >43</th>\n",
       "      <td id=\"T_00705_row1_col0\" class=\"data row1 col0\" >310917</td>\n",
       "      <td id=\"T_00705_row1_col1\" class=\"data row1 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row1_col2\" class=\"data row1 col2\" >77269</td>\n",
       "      <td id=\"T_00705_row1_col3\" class=\"data row1 col3\" >3434</td>\n",
       "      <td id=\"T_00705_row1_col4\" class=\"data row1 col4\" >25007</td>\n",
       "      <td id=\"T_00705_row1_col5\" class=\"data row1 col5\" >2596</td>\n",
       "      <td id=\"T_00705_row1_col6\" class=\"data row1 col6\" >1582559700</td>\n",
       "      <td id=\"T_00705_row1_col7\" class=\"data row1 col7\" >1</td>\n",
       "      <td id=\"T_00705_row1_col8\" class=\"data row1 col8\" >9915.000000</td>\n",
       "      <td id=\"T_00705_row1_col9\" class=\"data row1 col9\" >49666.000000</td>\n",
       "      <td id=\"T_00705_row1_col10\" class=\"data row1 col10\" >2020-02-24</td>\n",
       "      <td id=\"T_00705_row1_col11\" class=\"data row1 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row2\" class=\"row_heading level0 row2\" >44</th>\n",
       "      <td id=\"T_00705_row2_col0\" class=\"data row2 col0\" >320997</td>\n",
       "      <td id=\"T_00705_row2_col1\" class=\"data row2 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row2_col2\" class=\"data row2 col2\" >77785</td>\n",
       "      <td id=\"T_00705_row2_col3\" class=\"data row2 col3\" >2824</td>\n",
       "      <td id=\"T_00705_row2_col4\" class=\"data row2 col4\" >27655</td>\n",
       "      <td id=\"T_00705_row2_col5\" class=\"data row2 col5\" >2666</td>\n",
       "      <td id=\"T_00705_row2_col6\" class=\"data row2 col6\" >1582646100</td>\n",
       "      <td id=\"T_00705_row2_col7\" class=\"data row2 col7\" >1</td>\n",
       "      <td id=\"T_00705_row2_col8\" class=\"data row2 col8\" >9126.000000</td>\n",
       "      <td id=\"T_00705_row2_col9\" class=\"data row2 col9\" >47464.000000</td>\n",
       "      <td id=\"T_00705_row2_col10\" class=\"data row2 col10\" >2020-02-25</td>\n",
       "      <td id=\"T_00705_row2_col11\" class=\"data row2 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row3\" class=\"row_heading level0 row3\" >45</th>\n",
       "      <td id=\"T_00705_row3_col0\" class=\"data row3 col0\" >331042</td>\n",
       "      <td id=\"T_00705_row3_col1\" class=\"data row3 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row3_col2\" class=\"data row3 col2\" >78195</td>\n",
       "      <td id=\"T_00705_row3_col3\" class=\"data row3 col3\" >2491</td>\n",
       "      <td id=\"T_00705_row3_col4\" class=\"data row3 col4\" >30078</td>\n",
       "      <td id=\"T_00705_row3_col5\" class=\"data row3 col5\" >2718</td>\n",
       "      <td id=\"T_00705_row3_col6\" class=\"data row3 col6\" >1582732500</td>\n",
       "      <td id=\"T_00705_row3_col7\" class=\"data row3 col7\" >1</td>\n",
       "      <td id=\"T_00705_row3_col8\" class=\"data row3 col8\" >8752.000000</td>\n",
       "      <td id=\"T_00705_row3_col9\" class=\"data row3 col9\" >45399.000000</td>\n",
       "      <td id=\"T_00705_row3_col10\" class=\"data row3 col10\" >2020-02-26</td>\n",
       "      <td id=\"T_00705_row3_col11\" class=\"data row3 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row4\" class=\"row_heading level0 row4\" >46</th>\n",
       "      <td id=\"T_00705_row4_col0\" class=\"data row4 col0\" >341122</td>\n",
       "      <td id=\"T_00705_row4_col1\" class=\"data row4 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row4_col2\" class=\"data row4 col2\" >78631</td>\n",
       "      <td id=\"T_00705_row4_col3\" class=\"data row4 col3\" >2358</td>\n",
       "      <td id=\"T_00705_row4_col4\" class=\"data row4 col4\" >32916</td>\n",
       "      <td id=\"T_00705_row4_col5\" class=\"data row4 col5\" >2747</td>\n",
       "      <td id=\"T_00705_row4_col6\" class=\"data row4 col6\" >1582818908</td>\n",
       "      <td id=\"T_00705_row4_col7\" class=\"data row4 col7\" >1</td>\n",
       "      <td id=\"T_00705_row4_col8\" class=\"data row4 col8\" >8346.000000</td>\n",
       "      <td id=\"T_00705_row4_col9\" class=\"data row4 col9\" >42968.000000</td>\n",
       "      <td id=\"T_00705_row4_col10\" class=\"data row4 col10\" >2020-02-27</td>\n",
       "      <td id=\"T_00705_row4_col11\" class=\"data row4 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row5\" class=\"row_heading level0 row5\" >47</th>\n",
       "      <td id=\"T_00705_row5_col0\" class=\"data row5 col0\" >351202</td>\n",
       "      <td id=\"T_00705_row5_col1\" class=\"data row5 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row5_col2\" class=\"data row5 col2\" >78962</td>\n",
       "      <td id=\"T_00705_row5_col3\" class=\"data row5 col3\" >2308</td>\n",
       "      <td id=\"T_00705_row5_col4\" class=\"data row5 col4\" >36312</td>\n",
       "      <td id=\"T_00705_row5_col5\" class=\"data row5 col5\" >2791</td>\n",
       "      <td id=\"T_00705_row5_col6\" class=\"data row5 col6\" >1582905300</td>\n",
       "      <td id=\"T_00705_row5_col7\" class=\"data row5 col7\" >1</td>\n",
       "      <td id=\"T_00705_row5_col8\" class=\"data row5 col8\" >7952.000000</td>\n",
       "      <td id=\"T_00705_row5_col9\" class=\"data row5 col9\" >39859.000000</td>\n",
       "      <td id=\"T_00705_row5_col10\" class=\"data row5 col10\" >2020-02-28</td>\n",
       "      <td id=\"T_00705_row5_col11\" class=\"data row5 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row6\" class=\"row_heading level0 row6\" >48</th>\n",
       "      <td id=\"T_00705_row6_col0\" class=\"data row6 col0\" >361282</td>\n",
       "      <td id=\"T_00705_row6_col1\" class=\"data row6 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row6_col2\" class=\"data row6 col2\" >79394</td>\n",
       "      <td id=\"T_00705_row6_col3\" class=\"data row6 col3\" >1418</td>\n",
       "      <td id=\"T_00705_row6_col4\" class=\"data row6 col4\" >39308</td>\n",
       "      <td id=\"T_00705_row6_col5\" class=\"data row6 col5\" >2838</td>\n",
       "      <td id=\"T_00705_row6_col6\" class=\"data row6 col6\" >1582991700</td>\n",
       "      <td id=\"T_00705_row6_col7\" class=\"data row6 col7\" >1</td>\n",
       "      <td id=\"T_00705_row6_col8\" class=\"data row6 col8\" >7664.000000</td>\n",
       "      <td id=\"T_00705_row6_col9\" class=\"data row6 col9\" >37248.000000</td>\n",
       "      <td id=\"T_00705_row6_col10\" class=\"data row6 col10\" >2020-02-29</td>\n",
       "      <td id=\"T_00705_row6_col11\" class=\"data row6 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row7\" class=\"row_heading level0 row7\" >49</th>\n",
       "      <td id=\"T_00705_row7_col0\" class=\"data row7 col0\" >371327</td>\n",
       "      <td id=\"T_00705_row7_col1\" class=\"data row7 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row7_col2\" class=\"data row7 col2\" >79972</td>\n",
       "      <td id=\"T_00705_row7_col3\" class=\"data row7 col3\" >851</td>\n",
       "      <td id=\"T_00705_row7_col4\" class=\"data row7 col4\" >42162</td>\n",
       "      <td id=\"T_00705_row7_col5\" class=\"data row7 col5\" >2873</td>\n",
       "      <td id=\"T_00705_row7_col6\" class=\"data row7 col6\" >1583078100</td>\n",
       "      <td id=\"T_00705_row7_col7\" class=\"data row7 col7\" >1</td>\n",
       "      <td id=\"T_00705_row7_col8\" class=\"data row7 col8\" >7365.000000</td>\n",
       "      <td id=\"T_00705_row7_col9\" class=\"data row7 col9\" >34937.000000</td>\n",
       "      <td id=\"T_00705_row7_col10\" class=\"data row7 col10\" >2020-03-01</td>\n",
       "      <td id=\"T_00705_row7_col11\" class=\"data row7 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row8\" class=\"row_heading level0 row8\" >50</th>\n",
       "      <td id=\"T_00705_row8_col0\" class=\"data row8 col0\" >381407</td>\n",
       "      <td id=\"T_00705_row8_col1\" class=\"data row8 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row8_col2\" class=\"data row8 col2\" >80175</td>\n",
       "      <td id=\"T_00705_row8_col3\" class=\"data row8 col3\" >715</td>\n",
       "      <td id=\"T_00705_row8_col4\" class=\"data row8 col4\" >44845</td>\n",
       "      <td id=\"T_00705_row8_col5\" class=\"data row8 col5\" >2915</td>\n",
       "      <td id=\"T_00705_row8_col6\" class=\"data row8 col6\" >1583164500</td>\n",
       "      <td id=\"T_00705_row8_col7\" class=\"data row8 col7\" >1</td>\n",
       "      <td id=\"T_00705_row8_col8\" class=\"data row8 col8\" >7110.000000</td>\n",
       "      <td id=\"T_00705_row8_col9\" class=\"data row8 col9\" >32415.000000</td>\n",
       "      <td id=\"T_00705_row8_col10\" class=\"data row8 col10\" >2020-03-02</td>\n",
       "      <td id=\"T_00705_row8_col11\" class=\"data row8 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row9\" class=\"row_heading level0 row9\" >51</th>\n",
       "      <td id=\"T_00705_row9_col0\" class=\"data row9 col0\" >391487</td>\n",
       "      <td id=\"T_00705_row9_col1\" class=\"data row9 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row9_col2\" class=\"data row9 col2\" >80303</td>\n",
       "      <td id=\"T_00705_row9_col3\" class=\"data row9 col3\" >587</td>\n",
       "      <td id=\"T_00705_row9_col4\" class=\"data row9 col4\" >47434</td>\n",
       "      <td id=\"T_00705_row9_col5\" class=\"data row9 col5\" >2948</td>\n",
       "      <td id=\"T_00705_row9_col6\" class=\"data row9 col6\" >1583250902</td>\n",
       "      <td id=\"T_00705_row9_col7\" class=\"data row9 col7\" >1</td>\n",
       "      <td id=\"T_00705_row9_col8\" class=\"data row9 col8\" >6806.000000</td>\n",
       "      <td id=\"T_00705_row9_col9\" class=\"data row9 col9\" >29921.000000</td>\n",
       "      <td id=\"T_00705_row9_col10\" class=\"data row9 col10\" >2020-03-03</td>\n",
       "      <td id=\"T_00705_row9_col11\" class=\"data row9 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row10\" class=\"row_heading level0 row10\" >52</th>\n",
       "      <td id=\"T_00705_row10_col0\" class=\"data row10 col0\" >401532</td>\n",
       "      <td id=\"T_00705_row10_col1\" class=\"data row10 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row10_col2\" class=\"data row10 col2\" >80424</td>\n",
       "      <td id=\"T_00705_row10_col3\" class=\"data row10 col3\" >520</td>\n",
       "      <td id=\"T_00705_row10_col4\" class=\"data row10 col4\" >50010</td>\n",
       "      <td id=\"T_00705_row10_col5\" class=\"data row10 col5\" >2984</td>\n",
       "      <td id=\"T_00705_row10_col6\" class=\"data row10 col6\" >1583337300</td>\n",
       "      <td id=\"T_00705_row10_col7\" class=\"data row10 col7\" >1</td>\n",
       "      <td id=\"T_00705_row10_col8\" class=\"data row10 col8\" >6416.000000</td>\n",
       "      <td id=\"T_00705_row10_col9\" class=\"data row10 col9\" >27430.000000</td>\n",
       "      <td id=\"T_00705_row10_col10\" class=\"data row10 col10\" >2020-03-04</td>\n",
       "      <td id=\"T_00705_row10_col11\" class=\"data row10 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row11\" class=\"row_heading level0 row11\" >53</th>\n",
       "      <td id=\"T_00705_row11_col0\" class=\"data row11 col0\" >411507</td>\n",
       "      <td id=\"T_00705_row11_col1\" class=\"data row11 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row11_col2\" class=\"data row11 col2\" >80581</td>\n",
       "      <td id=\"T_00705_row11_col3\" class=\"data row11 col3\" >522</td>\n",
       "      <td id=\"T_00705_row11_col4\" class=\"data row11 col4\" >52305</td>\n",
       "      <td id=\"T_00705_row11_col5\" class=\"data row11 col5\" >3016</td>\n",
       "      <td id=\"T_00705_row11_col6\" class=\"data row11 col6\" >1583423700</td>\n",
       "      <td id=\"T_00705_row11_col7\" class=\"data row11 col7\" >1</td>\n",
       "      <td id=\"T_00705_row11_col8\" class=\"data row11 col8\" >5952.000000</td>\n",
       "      <td id=\"T_00705_row11_col9\" class=\"data row11 col9\" >25260.000000</td>\n",
       "      <td id=\"T_00705_row11_col10\" class=\"data row11 col10\" >2020-03-05</td>\n",
       "      <td id=\"T_00705_row11_col11\" class=\"data row11 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row12\" class=\"row_heading level0 row12\" >54</th>\n",
       "      <td id=\"T_00705_row12_col0\" class=\"data row12 col0\" >421587</td>\n",
       "      <td id=\"T_00705_row12_col1\" class=\"data row12 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row12_col2\" class=\"data row12 col2\" >80734</td>\n",
       "      <td id=\"T_00705_row12_col3\" class=\"data row12 col3\" >482</td>\n",
       "      <td id=\"T_00705_row12_col4\" class=\"data row12 col4\" >53968</td>\n",
       "      <td id=\"T_00705_row12_col5\" class=\"data row12 col5\" >3045</td>\n",
       "      <td id=\"T_00705_row12_col6\" class=\"data row12 col6\" >1583510100</td>\n",
       "      <td id=\"T_00705_row12_col7\" class=\"data row12 col7\" >1</td>\n",
       "      <td id=\"T_00705_row12_col8\" class=\"data row12 col8\" >5737.000000</td>\n",
       "      <td id=\"T_00705_row12_col9\" class=\"data row12 col9\" >23721.000000</td>\n",
       "      <td id=\"T_00705_row12_col10\" class=\"data row12 col10\" >2020-03-06</td>\n",
       "      <td id=\"T_00705_row12_col11\" class=\"data row12 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row13\" class=\"row_heading level0 row13\" >55</th>\n",
       "      <td id=\"T_00705_row13_col0\" class=\"data row13 col0\" >431632</td>\n",
       "      <td id=\"T_00705_row13_col1\" class=\"data row13 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row13_col2\" class=\"data row13 col2\" >80815</td>\n",
       "      <td id=\"T_00705_row13_col3\" class=\"data row13 col3\" >502</td>\n",
       "      <td id=\"T_00705_row13_col4\" class=\"data row13 col4\" >55558</td>\n",
       "      <td id=\"T_00705_row13_col5\" class=\"data row13 col5\" >3073</td>\n",
       "      <td id=\"T_00705_row13_col6\" class=\"data row13 col6\" >1583596500</td>\n",
       "      <td id=\"T_00705_row13_col7\" class=\"data row13 col7\" >1</td>\n",
       "      <td id=\"T_00705_row13_col8\" class=\"data row13 col8\" >5489.000000</td>\n",
       "      <td id=\"T_00705_row13_col9\" class=\"data row13 col9\" >22184.000000</td>\n",
       "      <td id=\"T_00705_row13_col10\" class=\"data row13 col10\" >2020-03-07</td>\n",
       "      <td id=\"T_00705_row13_col11\" class=\"data row13 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row14\" class=\"row_heading level0 row14\" >56</th>\n",
       "      <td id=\"T_00705_row14_col0\" class=\"data row14 col0\" >441712</td>\n",
       "      <td id=\"T_00705_row14_col1\" class=\"data row14 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row14_col2\" class=\"data row14 col2\" >80868</td>\n",
       "      <td id=\"T_00705_row14_col3\" class=\"data row14 col3\" >458</td>\n",
       "      <td id=\"T_00705_row14_col4\" class=\"data row14 col4\" >57412</td>\n",
       "      <td id=\"T_00705_row14_col5\" class=\"data row14 col5\" >3101</td>\n",
       "      <td id=\"T_00705_row14_col6\" class=\"data row14 col6\" >1583682900</td>\n",
       "      <td id=\"T_00705_row14_col7\" class=\"data row14 col7\" >1</td>\n",
       "      <td id=\"T_00705_row14_col8\" class=\"data row14 col8\" >5264.000000</td>\n",
       "      <td id=\"T_00705_row14_col9\" class=\"data row14 col9\" >20355.000000</td>\n",
       "      <td id=\"T_00705_row14_col10\" class=\"data row14 col10\" >2020-03-08</td>\n",
       "      <td id=\"T_00705_row14_col11\" class=\"data row14 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row15\" class=\"row_heading level0 row15\" >0</th>\n",
       "      <td id=\"T_00705_row15_col0\" class=\"data row15 col0\" >1</td>\n",
       "      <td id=\"T_00705_row15_col1\" class=\"data row15 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row15_col2\" class=\"data row15 col2\" >41</td>\n",
       "      <td id=\"T_00705_row15_col3\" class=\"data row15 col3\" >0</td>\n",
       "      <td id=\"T_00705_row15_col4\" class=\"data row15 col4\" >0</td>\n",
       "      <td id=\"T_00705_row15_col5\" class=\"data row15 col5\" >1</td>\n",
       "      <td id=\"T_00705_row15_col6\" class=\"data row15 col6\" >1578794400</td>\n",
       "      <td id=\"T_00705_row15_col7\" class=\"data row15 col7\" >1</td>\n",
       "      <td id=\"T_00705_row15_col8\" class=\"data row15 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row15_col9\" class=\"data row15 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row15_col10\" class=\"data row15 col10\" >2020-01-12</td>\n",
       "      <td id=\"T_00705_row15_col11\" class=\"data row15 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row16\" class=\"row_heading level0 row16\" >1</th>\n",
       "      <td id=\"T_00705_row16_col0\" class=\"data row16 col0\" >2</td>\n",
       "      <td id=\"T_00705_row16_col1\" class=\"data row16 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row16_col2\" class=\"data row16 col2\" >41</td>\n",
       "      <td id=\"T_00705_row16_col3\" class=\"data row16 col3\" >0</td>\n",
       "      <td id=\"T_00705_row16_col4\" class=\"data row16 col4\" >0</td>\n",
       "      <td id=\"T_00705_row16_col5\" class=\"data row16 col5\" >1</td>\n",
       "      <td id=\"T_00705_row16_col6\" class=\"data row16 col6\" >1578880800</td>\n",
       "      <td id=\"T_00705_row16_col7\" class=\"data row16 col7\" >1</td>\n",
       "      <td id=\"T_00705_row16_col8\" class=\"data row16 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row16_col9\" class=\"data row16 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row16_col10\" class=\"data row16 col10\" >2020-01-13</td>\n",
       "      <td id=\"T_00705_row16_col11\" class=\"data row16 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row17\" class=\"row_heading level0 row17\" >2</th>\n",
       "      <td id=\"T_00705_row17_col0\" class=\"data row17 col0\" >3</td>\n",
       "      <td id=\"T_00705_row17_col1\" class=\"data row17 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row17_col2\" class=\"data row17 col2\" >41</td>\n",
       "      <td id=\"T_00705_row17_col3\" class=\"data row17 col3\" >0</td>\n",
       "      <td id=\"T_00705_row17_col4\" class=\"data row17 col4\" >0</td>\n",
       "      <td id=\"T_00705_row17_col5\" class=\"data row17 col5\" >1</td>\n",
       "      <td id=\"T_00705_row17_col6\" class=\"data row17 col6\" >1578967200</td>\n",
       "      <td id=\"T_00705_row17_col7\" class=\"data row17 col7\" >1</td>\n",
       "      <td id=\"T_00705_row17_col8\" class=\"data row17 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row17_col9\" class=\"data row17 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row17_col10\" class=\"data row17 col10\" >2020-01-14</td>\n",
       "      <td id=\"T_00705_row17_col11\" class=\"data row17 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row18\" class=\"row_heading level0 row18\" >3</th>\n",
       "      <td id=\"T_00705_row18_col0\" class=\"data row18 col0\" >4</td>\n",
       "      <td id=\"T_00705_row18_col1\" class=\"data row18 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row18_col2\" class=\"data row18 col2\" >41</td>\n",
       "      <td id=\"T_00705_row18_col3\" class=\"data row18 col3\" >0</td>\n",
       "      <td id=\"T_00705_row18_col4\" class=\"data row18 col4\" >0</td>\n",
       "      <td id=\"T_00705_row18_col5\" class=\"data row18 col5\" >2</td>\n",
       "      <td id=\"T_00705_row18_col6\" class=\"data row18 col6\" >1579053600</td>\n",
       "      <td id=\"T_00705_row18_col7\" class=\"data row18 col7\" >1</td>\n",
       "      <td id=\"T_00705_row18_col8\" class=\"data row18 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row18_col9\" class=\"data row18 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row18_col10\" class=\"data row18 col10\" >2020-01-15</td>\n",
       "      <td id=\"T_00705_row18_col11\" class=\"data row18 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row19\" class=\"row_heading level0 row19\" >4</th>\n",
       "      <td id=\"T_00705_row19_col0\" class=\"data row19 col0\" >5</td>\n",
       "      <td id=\"T_00705_row19_col1\" class=\"data row19 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row19_col2\" class=\"data row19 col2\" >45</td>\n",
       "      <td id=\"T_00705_row19_col3\" class=\"data row19 col3\" >0</td>\n",
       "      <td id=\"T_00705_row19_col4\" class=\"data row19 col4\" >0</td>\n",
       "      <td id=\"T_00705_row19_col5\" class=\"data row19 col5\" >2</td>\n",
       "      <td id=\"T_00705_row19_col6\" class=\"data row19 col6\" >1579140000</td>\n",
       "      <td id=\"T_00705_row19_col7\" class=\"data row19 col7\" >1</td>\n",
       "      <td id=\"T_00705_row19_col8\" class=\"data row19 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row19_col9\" class=\"data row19 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row19_col10\" class=\"data row19 col10\" >2020-01-16</td>\n",
       "      <td id=\"T_00705_row19_col11\" class=\"data row19 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row20\" class=\"row_heading level0 row20\" >5</th>\n",
       "      <td id=\"T_00705_row20_col0\" class=\"data row20 col0\" >6</td>\n",
       "      <td id=\"T_00705_row20_col1\" class=\"data row20 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row20_col2\" class=\"data row20 col2\" >62</td>\n",
       "      <td id=\"T_00705_row20_col3\" class=\"data row20 col3\" >0</td>\n",
       "      <td id=\"T_00705_row20_col4\" class=\"data row20 col4\" >0</td>\n",
       "      <td id=\"T_00705_row20_col5\" class=\"data row20 col5\" >2</td>\n",
       "      <td id=\"T_00705_row20_col6\" class=\"data row20 col6\" >1579226400</td>\n",
       "      <td id=\"T_00705_row20_col7\" class=\"data row20 col7\" >1</td>\n",
       "      <td id=\"T_00705_row20_col8\" class=\"data row20 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row20_col9\" class=\"data row20 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row20_col10\" class=\"data row20 col10\" >2020-01-17</td>\n",
       "      <td id=\"T_00705_row20_col11\" class=\"data row20 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row21\" class=\"row_heading level0 row21\" >6</th>\n",
       "      <td id=\"T_00705_row21_col0\" class=\"data row21 col0\" >7</td>\n",
       "      <td id=\"T_00705_row21_col1\" class=\"data row21 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row21_col2\" class=\"data row21 col2\" >198</td>\n",
       "      <td id=\"T_00705_row21_col3\" class=\"data row21 col3\" >0</td>\n",
       "      <td id=\"T_00705_row21_col4\" class=\"data row21 col4\" >0</td>\n",
       "      <td id=\"T_00705_row21_col5\" class=\"data row21 col5\" >3</td>\n",
       "      <td id=\"T_00705_row21_col6\" class=\"data row21 col6\" >1579312800</td>\n",
       "      <td id=\"T_00705_row21_col7\" class=\"data row21 col7\" >1</td>\n",
       "      <td id=\"T_00705_row21_col8\" class=\"data row21 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row21_col9\" class=\"data row21 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row21_col10\" class=\"data row21 col10\" >2020-01-18</td>\n",
       "      <td id=\"T_00705_row21_col11\" class=\"data row21 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row22\" class=\"row_heading level0 row22\" >7</th>\n",
       "      <td id=\"T_00705_row22_col0\" class=\"data row22 col0\" >8</td>\n",
       "      <td id=\"T_00705_row22_col1\" class=\"data row22 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row22_col2\" class=\"data row22 col2\" >201</td>\n",
       "      <td id=\"T_00705_row22_col3\" class=\"data row22 col3\" >0</td>\n",
       "      <td id=\"T_00705_row22_col4\" class=\"data row22 col4\" >0</td>\n",
       "      <td id=\"T_00705_row22_col5\" class=\"data row22 col5\" >3</td>\n",
       "      <td id=\"T_00705_row22_col6\" class=\"data row22 col6\" >1579399200</td>\n",
       "      <td id=\"T_00705_row22_col7\" class=\"data row22 col7\" >1</td>\n",
       "      <td id=\"T_00705_row22_col8\" class=\"data row22 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row22_col9\" class=\"data row22 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row22_col10\" class=\"data row22 col10\" >2020-01-19</td>\n",
       "      <td id=\"T_00705_row22_col11\" class=\"data row22 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row23\" class=\"row_heading level0 row23\" >8</th>\n",
       "      <td id=\"T_00705_row23_col0\" class=\"data row23 col0\" >9</td>\n",
       "      <td id=\"T_00705_row23_col1\" class=\"data row23 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row23_col2\" class=\"data row23 col2\" >218</td>\n",
       "      <td id=\"T_00705_row23_col3\" class=\"data row23 col3\" >6</td>\n",
       "      <td id=\"T_00705_row23_col4\" class=\"data row23 col4\" >0</td>\n",
       "      <td id=\"T_00705_row23_col5\" class=\"data row23 col5\" >4</td>\n",
       "      <td id=\"T_00705_row23_col6\" class=\"data row23 col6\" >1579485600</td>\n",
       "      <td id=\"T_00705_row23_col7\" class=\"data row23 col7\" >1</td>\n",
       "      <td id=\"T_00705_row23_col8\" class=\"data row23 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row23_col9\" class=\"data row23 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row23_col10\" class=\"data row23 col10\" >2020-01-20</td>\n",
       "      <td id=\"T_00705_row23_col11\" class=\"data row23 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row24\" class=\"row_heading level0 row24\" >9</th>\n",
       "      <td id=\"T_00705_row24_col0\" class=\"data row24 col0\" >11</td>\n",
       "      <td id=\"T_00705_row24_col1\" class=\"data row24 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row24_col2\" class=\"data row24 col2\" >291</td>\n",
       "      <td id=\"T_00705_row24_col3\" class=\"data row24 col3\" >9</td>\n",
       "      <td id=\"T_00705_row24_col4\" class=\"data row24 col4\" >0</td>\n",
       "      <td id=\"T_00705_row24_col5\" class=\"data row24 col5\" >4</td>\n",
       "      <td id=\"T_00705_row24_col6\" class=\"data row24 col6\" >1579572000</td>\n",
       "      <td id=\"T_00705_row24_col7\" class=\"data row24 col7\" >1</td>\n",
       "      <td id=\"T_00705_row24_col8\" class=\"data row24 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row24_col9\" class=\"data row24 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row24_col10\" class=\"data row24 col10\" >2020-01-21</td>\n",
       "      <td id=\"T_00705_row24_col11\" class=\"data row24 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row25\" class=\"row_heading level0 row25\" >10</th>\n",
       "      <td id=\"T_00705_row25_col0\" class=\"data row25 col0\" >1268</td>\n",
       "      <td id=\"T_00705_row25_col1\" class=\"data row25 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row25_col2\" class=\"data row25 col2\" >544</td>\n",
       "      <td id=\"T_00705_row25_col3\" class=\"data row25 col3\" >137</td>\n",
       "      <td id=\"T_00705_row25_col4\" class=\"data row25 col4\" >28</td>\n",
       "      <td id=\"T_00705_row25_col5\" class=\"data row25 col5\" >17</td>\n",
       "      <td id=\"T_00705_row25_col6\" class=\"data row25 col6\" >1579708500</td>\n",
       "      <td id=\"T_00705_row25_col7\" class=\"data row25 col7\" >1</td>\n",
       "      <td id=\"T_00705_row25_col8\" class=\"data row25 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row25_col9\" class=\"data row25 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row25_col10\" class=\"data row25 col10\" >2020-01-22</td>\n",
       "      <td id=\"T_00705_row25_col11\" class=\"data row25 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row26\" class=\"row_heading level0 row26\" >11</th>\n",
       "      <td id=\"T_00705_row26_col0\" class=\"data row26 col0\" >15</td>\n",
       "      <td id=\"T_00705_row26_col1\" class=\"data row26 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row26_col2\" class=\"data row26 col2\" >634</td>\n",
       "      <td id=\"T_00705_row26_col3\" class=\"data row26 col3\" >422</td>\n",
       "      <td id=\"T_00705_row26_col4\" class=\"data row26 col4\" >30</td>\n",
       "      <td id=\"T_00705_row26_col5\" class=\"data row26 col5\" >17</td>\n",
       "      <td id=\"T_00705_row26_col6\" class=\"data row26 col6\" >1579786500</td>\n",
       "      <td id=\"T_00705_row26_col7\" class=\"data row26 col7\" >1</td>\n",
       "      <td id=\"T_00705_row26_col8\" class=\"data row26 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row26_col9\" class=\"data row26 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row26_col10\" class=\"data row26 col10\" >2020-01-23</td>\n",
       "      <td id=\"T_00705_row26_col11\" class=\"data row26 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row27\" class=\"row_heading level0 row27\" >12</th>\n",
       "      <td id=\"T_00705_row27_col0\" class=\"data row27 col0\" >16</td>\n",
       "      <td id=\"T_00705_row27_col1\" class=\"data row27 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row27_col2\" class=\"data row27 col2\" >897</td>\n",
       "      <td id=\"T_00705_row27_col3\" class=\"data row27 col3\" >1076</td>\n",
       "      <td id=\"T_00705_row27_col4\" class=\"data row27 col4\" >36</td>\n",
       "      <td id=\"T_00705_row27_col5\" class=\"data row27 col5\" >26</td>\n",
       "      <td id=\"T_00705_row27_col6\" class=\"data row27 col6\" >1579881300</td>\n",
       "      <td id=\"T_00705_row27_col7\" class=\"data row27 col7\" >1</td>\n",
       "      <td id=\"T_00705_row27_col8\" class=\"data row27 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row27_col9\" class=\"data row27 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row27_col10\" class=\"data row27 col10\" >2020-01-24</td>\n",
       "      <td id=\"T_00705_row27_col11\" class=\"data row27 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row28\" class=\"row_heading level0 row28\" >13</th>\n",
       "      <td id=\"T_00705_row28_col0\" class=\"data row28 col0\" >17</td>\n",
       "      <td id=\"T_00705_row28_col1\" class=\"data row28 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row28_col2\" class=\"data row28 col2\" >1408</td>\n",
       "      <td id=\"T_00705_row28_col3\" class=\"data row28 col3\" >2032</td>\n",
       "      <td id=\"T_00705_row28_col4\" class=\"data row28 col4\" >39</td>\n",
       "      <td id=\"T_00705_row28_col5\" class=\"data row28 col5\" >42</td>\n",
       "      <td id=\"T_00705_row28_col6\" class=\"data row28 col6\" >1579967705</td>\n",
       "      <td id=\"T_00705_row28_col7\" class=\"data row28 col7\" >1</td>\n",
       "      <td id=\"T_00705_row28_col8\" class=\"data row28 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row28_col9\" class=\"data row28 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row28_col10\" class=\"data row28 col10\" >2020-01-25</td>\n",
       "      <td id=\"T_00705_row28_col11\" class=\"data row28 col11\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00705_level0_row29\" class=\"row_heading level0 row29\" >14</th>\n",
       "      <td id=\"T_00705_row29_col0\" class=\"data row29 col0\" >40935</td>\n",
       "      <td id=\"T_00705_row29_col1\" class=\"data row29 col1\" >中国</td>\n",
       "      <td id=\"T_00705_row29_col2\" class=\"data row29 col2\" >2076</td>\n",
       "      <td id=\"T_00705_row29_col3\" class=\"data row29 col3\" >2692</td>\n",
       "      <td id=\"T_00705_row29_col4\" class=\"data row29 col4\" >49</td>\n",
       "      <td id=\"T_00705_row29_col5\" class=\"data row29 col5\" >56</td>\n",
       "      <td id=\"T_00705_row29_col6\" class=\"data row29 col6\" >1580054100</td>\n",
       "      <td id=\"T_00705_row29_col7\" class=\"data row29 col7\" >1</td>\n",
       "      <td id=\"T_00705_row29_col8\" class=\"data row29 col8\" >nan</td>\n",
       "      <td id=\"T_00705_row29_col9\" class=\"data row29 col9\" >nan</td>\n",
       "      <td id=\"T_00705_row29_col10\" class=\"data row29 col10\" >2020-01-26</td>\n",
       "      <td id=\"T_00705_row29_col11\" class=\"data row29 col11\" >1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2ca30576e08>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_summary.style.background_gradient(cmap='Reds')"
   ]
  }
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